Regression models for partially localized fMRI connectivity analyses.

ABSTRACT:

Background

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated.

Methods

In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically oppo

SUBMITTER: Smith BB 

PROVIDER: S-EPMC10679340 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature